NIPS Workshop, 12 December 2015, Montréal, Canada

Keynote Talks

Research Talks

Research Panel

Frank Wood

Ruslan Salakutdinov

Dave Blei

Zoubin Ghahramani

Language Talks and Panel

Koray Kavukcuoglu

Michael Betancourt

Vikash Mansinghka

Avi Pfeffer

Chung-chieh Shan

Yi Wu

Daniel Tarlow

Overview

Probabilistic models have traditionally co-evolved with tailored algorithms for efficient learning and inference. One of the exciting developments of recent years has been the resurgence of black box methods, which make relatively few assumptions about the model structure, allowing application to broader model families.

In probabilistic programming systems, black box methods have greatly improved the capabilities of inference back ends. Similarly, the design of connectionist models has been simplified by the development of black box frameworks for training arbitrary architectures. These innovations open up opportunities to design new classes of models that smoothly negotiate the transition from low-level features of the data to high-level structured representations that are interpretable and generalize well across examples.

This workshop brings together developers of black box inference technologies, probabilistic programming systems, and connectionist computing frameworks. The goal is to formulate a shared understanding of how black box methods can enable advances in the design of intelligent learning systems. Topics of discussion will include: